Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

A Simple and Robustness Algorithm for ECG R- peak Detection

  • Rahman, Md Saifur (Dept. of Electrical and Electronic Engineering, Kongju National University) ;
  • Choi, Chulhyung (Dept. of Electrical and Electronic Engineering, Kongju National University) ;
  • Kim, Young-pil (Dept. of Electrical and Electronic Engineering, Kongju National University) ;
  • Kim, Sikyung (Dept. of Electrical and Electronic Engineering, Kongju National University)
  • Received : 2017.08.23
  • Accepted : 2018.04.25
  • Published : 2018.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

There have been numerous studies that extract the R-peak from electrocardiogram (ECG) signals. All of these studies can extract R-peak from ECG. However, these methods are complicated and difficult to implement in a real-time portable ECG device. After filtration choosing a threshold value for R-peak detection is a big challenge. Fixed threshold scheme is sometimes unable to detect low R-peak value and adaptive threshold sometime detect wrong R-peak for more adaptation. In this paper, a simple and robustness algorithm is proposed to detect R-peak with less complexity. This method also solves the problem of threshold value selection. Using the adaptive filter, the baseline drift can be removed from ECG signal. After filtration, an appropriate threshold value is automatically chosen by using the minimum and maximum value of an ECG signals. Then the neighborhood searching scheme is applied under threshold value to detect R-peak from ECG signals. Proposed method improves the detection and accuracy rate of R-peak detection. After R-peak detection, we calculate heart rate to know the heart condition.

Keywords

References

  1. S. Yazdani and J. M. Vesin, "A Novel Preprocessing Tool to Enhance ECG R-wave Extraction," in Computing in Cardiology Conference (CinC), 2016, 11-14 Sept. 2016.
  2. N. Ravanshad, H. Rezaee-Dehsorkh, R. Lotfi, and Y. Lian, "A level-crossing based QRS-detection algorithm for wearable ECG sensors," IEEE J. Biomed. Heal. Informatics, vol. 18, no. 1, pp. 183-192, 2014. https://doi.org/10.1109/JBHI.2013.2274809
  3. J. P. Martinez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, "A Wavelet-Based ECG Delineator Evaluation on Standard Databases," IEEE Trans. Biomed. Eng., vol. 51, no. 4, pp. 570-581, 2004. https://doi.org/10.1109/TBME.2003.821031
  4. M. Bahoura, M. Hassani, and M. Hubin, "DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis," Comput. Methods Programs Biomed., vol. 52, no. 1, pp. 35-44, 1997. https://doi.org/10.1016/S0169-2607(97)01780-X
  5. J. Pan and W. J. Tompkins, "A Real-Time QRS Detection Algorithm," IEEE Trans. Biomed. Eng., vol. BME-32, no. 3, pp. 230-236, 1985. https://doi.org/10.1109/TBME.1985.325532
  6. F. Zhang and Y. Lian, "QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks," in IEEE Transactions on Biomedical Circuits and Systems, 2009, vol. 3, no. 4, pp. 220-228. https://doi.org/10.1109/TBCAS.2009.2020093
  7. S. Yazdani and J. M. Vesin, "Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology," Digit. Signal Process. A Rev. J., vol. 56, pp. 100-109, 2016. https://doi.org/10.1016/j.dsp.2016.06.010
  8. G. B. Moody and R. G. Mark, "Development and evaluation of a 2-lead ECG analysis program," in Computers in cardiology, 1982, pp. 39-44.
  9. J. Lee, K. Jeong, J. Yoon, and M. Lee, "A simple real time QRS detection algorithm," in 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam 1996,
  10. C. Li, C. Zheng, and C. Tai, "Detection of ECG characteristic points using wavelet transforms," IEEE Trans Biomed Eng, vol. 42, no. Bmei, pp. 21-28, 1995. https://doi.org/10.1109/10.362922